quantwave_core/
traits.rs

1/// The core trait for streaming indicators.
2/// Every indicator maintains an internal state and processes data points one by one.
3pub trait Next<Input> {
4    type Output;
5
6    /// Process the next input and return the updated output.
7    fn next(&mut self, input: Input) -> Self::Output;
8
9    /// Process a batch of inputs eagerly.
10    /// This allows implementations to override with vectorized logic,
11    /// while falling back to scalar processing by default.
12    fn next_batch(&mut self, inputs: &[Input]) -> Vec<Self::Output> 
13    where
14        Input: Copy,
15    {
16        let mut outputs = Vec::with_capacity(inputs.len());
17        for &input in inputs {
18            outputs.push(self.next(input));
19        }
20        outputs
21    }
22}
23
24/// A trait for algorithms that smooth a series of values (e.g., SMA, EMA).
25/// This allows indicators like SuperTrend or Keltner Channels to be generic over their MA type.
26pub trait SmoothingAlgorithm: Next<f64, Output = f64> + Clone + Send + Sync {}
27
28/// A trait for indicator configurations that can build their respective streaming state machines.
29pub trait IndicatorConfig {
30    type Indicator: Next<f64>;
31
32    /// Build a new instance of the indicator state machine.
33    fn build(&self) -> Self::Indicator;
34}
35
36/// Blanket implementation for types that satisfy the SmoothingAlgorithm requirements.
37impl<T> SmoothingAlgorithm for T where T: Next<f64, Output = f64> + Clone + Send + Sync {}
quantwave_core/traits.rs

quantwave_core/
traits.rs
